System And Method For Measuring Clarity Of Images Used In An Iris Recognition System

ABSTRACT

An image quality measuring method enables a biometric image to be evaluated to determine whether the biometric image data are adequate for identification processing. The method includes converting a biometric image to dimensionless image data, filtering the dimensionless image data with a band pass filter, identifying a plurality of portions in the filtered data as containing identification features, each portion in the plurality having an information measurement that indicates feature content greater than portions in the filtered data that are excluded from the plurality, and measuring clarity for the biometric image from the identified plurality of portions in the filtered data.

TECHNICAL FIELD

The system and method disclosed below relates to biometricidentification, and, more particularly, to identification systems thatuse images of a person's iris to identify the person.

BACKGROUND

Biometric identification systems are known. In these systems, an imageis typically taken of some aspect of a person's physiology andinformation from the image is compared to stored data corresponding tothat physiological aspect. The degree of correlation between theacquired image and the stored data determines whether the personcorresponding to the acquired image is the person from which the storeddata has been obtained. The stored data may correspond to a person'sfingerprint, face, and/or voice. Each type of biometric possessesadvantages and disadvantages. For example, fingerprints require contactwith a person to obtain the image of the fingerprint for comparison tothe stored data. Because contact with a person to be identified is notalways possible, this form of identification may be problematic.

One reliable way of identifying persons at a distance has beenidentification of a person through an image of a human eye iris. Theiris of a human eye possesses a pattern of high complexity that changesvery little over the life of a person. Iris patterns are so unique thatthe iris patterns of the left and right eyes of the same person aredifferent. Additionally, the iris patterns can be obtained at a distanceusing a near infrared (NIR) camera with an appropriate lens. The iris isprotected by the cornea of an eye. The uniqueness and relatively minorchanges in the iris under different environmental conditions makes theiris a good candidate for automated and highly reliable personalidentification.

In previously known iris identification systems, such as the onedisclosed in U.S. Pat. No. 5,291,560 to Daugman, an image of a person'seye is obtained and then processed to identify the portion of the eyethat corresponds to the iris. Data from the iris that are not occludedby the eyelids may be used to generate a raw data signal. This signalmay then be filtered using a pair of two-dimensional Gabor filters toextract pattern information from the raw data signal. The resulting datasignal may be compared to stored data for identification purposes. Inthe Daugman reference, Hamming distances are selected to vary thecriteria for evaluating an identification match.

The quality of the iris image that is used for identification evaluationconsiderably affects the accuracy of the system. Failures to detectimposters and false identification of imposters are more likely to occurwith blurred iris images. Many factors affect the quality of an irisimage. These factors include blurriness, resolution, image contrast,iris occlusion, and iris deformation. Blurriness, however, remains oneof the most significant problems for iris image acquisition. Methodsthat have been used to evaluate the quality of an iris image have beenadversely affected by occlusion of the iris that occurs from the eyelidscovering a portion of the iris. Being able to assess the quality of aniris image would help ensure that an iris identification system isobtaining an iris image containing sufficient information foridentifying a person.

SUMMARY

An image quality measuring method enables an evaluation of an iris imageto determine whether the iris image is adequate for identificationprocessing. The method may also be used to evaluate other types ofbiometric data. The method includes converting a biometric image todimensionless image data, filtering the dimensionless image data with aband pass filter, identifying a plurality of portions in the filtereddata as containing identification features, each portion in theplurality having an information measurement that indicates featurecontent greater than portions in the filtered data that are excludedfrom the plurality, and measuring clarity for the biometric image fromthe identified plurality of portions in the filtered data.

The method may be implemented by a system that evaluates biometric imagedata before a biometric recognition system processes the image foridentification purposes. The system includes a preprocessor configuredto convert biometric image data to dimensionless image data, a featureextractor configured to filter the dimensionless data with a band passfilter, a region of interest selector configured to identify a pluralityof portions of the filtered image data, each portion in the pluralityhaving an information distance that is indicative of feature content,and a feature information measurement generator configured to measureclarity of the biometric image from the portions in the plurality ofportions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system that computes a quality score foran iris image.

FIG. 2 shows an image of a human eye and the edges to be detected in theimage.

FIG. 3A is a depiction of a segmented iris image.

FIG. 3B is the image of FIG. 3A converted to classic polar coordinatespace.

FIG. 3C is the image of a mask derived from the image of FIG. 3A thathas been converted to classic polar coordinate space.

FIG. 3D is the image of FIG. 3A converted to log-polar coordinate space.

FIG. 3E is the image of a mask derived from the image of FIG. 3A thathas been converted to log-polar coordinate space.

FIG. 4A is a graphical representation of processing performed by thefeature extractor shown in FIG. 1.

FIG. 4B depicts the image of FIG. 3D after it has been through theprocess shown in FIG. 4A.

FIG. 5A is an image of a human eye in which identification features arescattered throughout the eye.

FIG. 5B is an image of a human eye in which identification features areclustered closely to the pupil.

FIG. 6 is a graphical representation of a normalization function used bythe occlusion measurement generator of FIG. 1.

FIG. 7 is a graphical representation of a normalization function used bythe dilation measurement generator of FIG. 1.

DETAILED DESCRIPTION

A system 10 for measuring quality of an iris image is shown in FIG. 1.The system is comprised of an image preprocessor 14, a claritymeasurement generator 16, an occlusion measurement generator 28, adilation measurement generator 30, and a quality score generator 34. Theclarity measurement generator 16 includes a feature extractor 18, aregion of interest selector 20, and a feature information measurementgenerator 24. In greater detail, the image preprocessor 14 receives animage of a human iris from a video camera or other image generatingdevice. The preprocessor 14 extracts features from the iris image soappropriate regions of interest can be selected and informationdistances for the selected regions measured. The occlusion measurementgenerator 28 and the dilation measurement generator 30 also receive theiris image for generation of occlusion and dilation in the image. Theimage score generator 34 weights the feature information measurement,the occlusion measurement, and dilation measurement to generate aquality score for the image. This score indicates whether the iris imagecontains sufficient information for a reliable identification.

The system 10 may be implemented on a computer system having a processorand associated input/output circuitry and memory. For example, thesystem 10 may be a computer system having a CPU processor operating at 1GHz. The system may also include a 1 GB hard drive and 256 MB ofvolatile memory, such as static RAM. The received image may be a 200×300image. Alternatively, the clarity measurement generator, the dilationmeasurement generator and/or the occlusion measurement generator may beimplemented with one or more application specific integrated circuits(ASIC) for performing the methods set forth in more detail below. Inanother embodiment, the clarity measurement generator, the dilationmeasurement generator, and the occlusion measurement generator may beimplemented with programmed instructions stored in program memory forthe general purpose processor. These instructions may be executed toextract and transform a portion of the iris image, measure the clarity,occlusion, and dilation of the image, and generate a quality score forthe image. In addition, the system 10 may be integrated with an existingiris recognition system for quality measurement. When integrated withexisting iris recognition systems, the preprocessor 14 may be replacedwith the image data preprocessor of the existing system, if it performsthe appropriate data transformations, to enable more computationallyefficient processing.

In the preprocessor 14, the portion of the eye image corresponding tothe iris is extracted from the image by detection of the pupil andlimbic boundaries, the eyelids, and the eyelashes. The limbic boundaryis a boundary between the outer circumference of the iris and the scleraor white portion of the eye. The pupil boundary is the meeting of theouter circumference of the pupil and the inner circumference of theiris. Eyelids and eyelashes typically occlude portions of the top andbottom regions of an iris image. To select the most accessible areas ofan iris image, the image data are processed with a Canny edge detectionmethod. Two thresholds are used for this method. A higher threshold isused to detect the pupil boundary and a lower threshold is used fordetecting the limbic boundary. These physical boundaries and theircorresponding edges in the image are depicted in FIG. 2.

The area that lies between the pupil and limbic boundaries thatcorresponds to iris data is segmented into multiple arcs. These arcs arelocated at various radii for a polar axis centered in the pupil. Theradii may range, for example, from the pupil boundary to the quarter,half, three-quarters, and full length of the limbic radii. The segmentedportions of the iris image are transformed from rectangular coordinatesto polar coordinates to form rectangular image areas for processing.Because the iris pattern resolution and the actual distance between thepupil boundary and the limbic boundaries may be changed for the sameiris by changes in image resolution, variances in camera-to-facedistance, and pupil contraction/dilation, normalization of the segmentediris region is required when the transformation to polar coordinates iscomputed. For each pixel in the original iris image located atrectangular coordinates (x_(i), y_(i)), the polar coordinates (r_(i),θ_(i)) may be computed as:

$r_{i} = {\frac{\overset{\sim}{L}}{L}\left( {\sqrt{\left( {x_{i} - x_{0}} \right)^{2} + \left( {y_{i} - y_{0}} \right)^{2}} - r_{0}} \right)}$$\theta_{i} = \left\{ {\begin{matrix}{\mspace{40mu} {{\frac{1}{\Delta\Theta}{\arcsin \left( \frac{y_{i} - y_{0}}{x_{i} - x_{0}} \right)}\mspace{14mu} y_{i}} \geq y_{0}}} \\{{{\frac{1}{\Delta\theta}\left\lbrack {{\arcsin \left( \frac{y_{i} - y_{0}}{x_{i} - x_{0}} \right)} + \pi} \right\rbrack}y_{i}} < y_{0}}\end{matrix}.} \right.$

In this transform, (x₀, y₀) is the center of the pupil, r₀ is thepupil's radius, and L is the actual distance between the pupil andlimbic boundaries in the original image. {tilde over (L)} is theselected normalized distance. The selected angular resolution in thedouble dimensionless polar axis is ΔΘ. As a result, the normalizedlength in the angular direction is

$\frac{2\pi}{\Delta\Theta}.$

Thus, the original image is transferred to a polar coordinate systemwith sides {tilde over (L)} by

$\frac{2\pi}{\Delta\Theta}.$

The resulting polar coordinates are sometimes called classic polarcoordinates.

Log-polar coordinates (r_(i) ^(log), Θ_(i) ^(log)) may also becalculated for the normalization as

${r_{i}^{\log} = \frac{\overset{\sim}{L}\; {\log \left( {r_{i}/r_{0}} \right)}}{\log \left( {r_{\lim}/r_{0}} \right)}},$

where r_(lim) is the limbic radius. θ_(i) ^(log) can be calculated asθ_(i) was calculated above. Thus, the iris data are transferred to adouble dimensionless log-polar coordinate space with size {tilde over(L)} by

$\frac{2\pi}{\Delta\theta}.$

FIG. 3A shows a segmented iris region, while FIG. 3B is thecorresponding polar coordinate image of the segmented iris region andFIG. 3D is the corresponding log-polar image. Comparing FIG. 3D to FIG.3B reveals the log-polar image has a higher resolution. The preprocessor14 may be configured to convert the image data to only one of the twodimensionless coordinate systems or to both of the dimensionlesscoordinate systems. Using the segmented iris image, a mask may also bedeveloped and transformed to the classic polar coordinate space as wellas the log-polar coordinate space. FIG. 3C and FIG. 3E depictrepresentations of the mask in these coordinate spaces. The maskrepresentations are provided to the occlusion measurement generator 28and the dilation measurement generator 30 for generation of theocclusion measurement and dilation measurement, respectively.

The image normalization achieved through the described transformationshelp maximize the rows of data in image data for further processing.Although the increments from the pupil boundary to the limbic boundaryare the same for each image, the angle θ varies in accordance with theamount of occlusion. By varying the angle θ, the length of the rows in atransformed arc are maximized to provide the most information foranalysis in each iris image.

After the preprocessor 14 has extracted and transformed the arcs for theiris image, the transformed arcs are provided to the feature extractor18 for clarity analysis. The feature extractor 18 performs a frequencytransform on each row of each polar image and then the transformed rowsare filtered with a one-dimensional (1D) Gabor transform filter. Thefiltered row data are returned to the polar coordinate space for furtheranalysis.

The processing of the feature selector 18 is shown in FIG. 4A. In moredetail, a one dimensional fast Fourier transform (FFT) is applied to arow of data within one of the polar or log-polar images (block 200). TheFFT transforms the dimensionless data to the spatial domain so thefrequency characteristics from −π to π radians can be analyzed. Thehighest and lowest frequencies are removed using a Log-Gabor band passfilter (block 204). The highest frequencies are removed to diminish theeffects of noise and artifacts that are not intrinsic to an irispattern. The lowest frequencies are removed to diminish the effects ofany DC component in the signal. This processing enables the frequenciesthat represent the iris pattern in an arc to be isolated.

The Log-Gabor transform applied to the transformed rows has themathematical form:

${G(\omega)} = ^{\frac{- {\log {({\omega/\omega_{0}})}}^{2}}{2\; {\log(\sigma)}^{2}}}$

Other types of band pass filters may be used alternatively. Band passfilters have been shown to be effective in iris pattern extraction asdescribed in the Daugman patent. In the form of the band pass filterdescribed by the equation above, σ is used to control the filter bandwidth and ω₀ is the filter center frequency. The center frequency isderived from the filter wavelength λ. For a filter that removes thehighest and lowest frequencies, the following parameters have beendetermined from empirical analysis:

${\sigma = 0.2},\; {\omega_{0} = \frac{1}{\lambda}},\; {s = {{number}\mspace{14mu} {of}\mspace{14mu} {pixels}\mspace{11mu} {in}\mspace{11mu} a\mspace{11mu} {row}}},\mspace{14mu} {{{and}\mspace{14mu} \lambda} = {\frac{s}{40}.}}$

The filtered row is returned from the spatial domain to thedimensionless polar coordinate space with an inverse FFT (block 208).FIG. 4B depicts a filtered representation of the log-polar image shownin FIG. 3D.

A blurry iris image is much more homogenous than a clear image.Consequently, the distribution of patterns in a blurry image is closerto a uniform distribution than the distribution in a clear image.Therefore, the information distance between the selected features andthe uniform distribution provides a measure of the image's clarity. Theinformation distance not only describes the randomness of the features,but it also may be used to generate high-order statistics of an irisimage based on its features. If the magnitude of a selected feature isr, the probability mass function is

${\overset{-}{p} = \frac{\overset{-}{r}}{{\overset{-}{r}}_{2}}},$

where the denominator is 2-norm. If the size of a selected feature is L,then the uniform distribution can be calculated as

$\overset{-}{q} = {\frac{1}{L}.}$

The information distance is

${{J\left( {\overset{-}{p},\overset{-}{q}} \right)} = {D\left( {{\overset{-}{p}\left. \overset{-}{q} \right)} + {{D\left( \overset{-}{q} \right.}\overset{-}{p}}} \right)}},\mspace{14mu} {{{where}\mspace{14mu} {D\left( {{\overset{-}{p}\left. \overset{-}{q} \right)} = {\sum{{\overset{-}{p}}_{i}\log_{2}\frac{{\overset{-}{p}}_{i}}{{\overset{-}{q}}_{i}}\mspace{14mu} {and}\mspace{14mu} {D\left( \overset{-}{q} \right.}\overset{-}{p}}}} \right)}} = {\sum{{\overset{-}{q}}_{i}\log_{2}{\frac{{\overset{-}{q}}_{i}}{{\overset{-}{p}}_{i}}.}}}}$

These last two entities are relative entropies that are generally notequal to one another, but J( p, q)=J( q, p).

As shown in FIG. 5A, some iris images contain distinguishable patternsthroughout the iris region, while others, as shown in FIG. 5B, onlycontain distinguishable patterns in the inner rings closest to thepupil. Thus, if the entire iris region is evaluated, images containingpattern data only in areas close to the pupil may be difficult todistinguish from blurry images. Consequently, region of interestselector 20 uses a sliding window to select non-occluded portions ofiris features having the highest information distances as calculatedabove. If a window length L has a start location at (u,v), the filteredmagnitude values of the L pixels in the window form a vector r. Theprobability mass functions p of this selected portion and the uniformdistribution calculated above may be used to identify the informationdistance J^((u,v))( p, q) for the portion within the window. Theinformation distance indicates whether the portion within the windowcontains identification feature content. The portion of the row havingthe largest information distance is stored as the representativeinformation distance for a row. If the total number of consecutivenon-occluded pixels in a row is smaller than L, the information distancefor that row is set to zero.

Because the inner circles for an iris image contain more distinguishablepatterns, their distance measurements are more heavily weighted. Theweights may be determine in the following manner:WJ_(i)=(1−ke^(l*i))*J_(i), where k and l are constants. By empiricaldeterminations, k=1/33 and l=0.1. J_(i) is the representativeinformation distance for the ith row. The N rows having the largestweighted representative information distances, which indicate featurecontent, are then selected as regions of interest.

To prevent severely blurred images from producing large informationdistances, the distributions are normalized. Large information distancesmay arise from the random perturbations in the data, which isessentially noise. Therefore, a threshold value T_(max) may be used topreclude this condition. The threshold is used by either comparing thedistance computed for row portion having the highest score or a meanscore for a row to the threshold. If the score is less than thethreshold, the representative information for the row is set to zero.The selected regions of interest are provided to the feature informationmeasurement score generator 24. This generator calculates themeasurement as

${{FIM} = {\frac{1}{N}{\sum\limits_{i}{WJ}_{i}}}},$

where i is the row with the ith top weighted representative informationdistance and N is the total number of portions used for featureinformation calculation.

The occlusion measurement generator 28 also receives the dimensionlessiris image data from the preprocessor 14. Generator 28 uses thedimensionless image and dimensionless mask data to measure how much ofthe iris area contains insufficient data for recognition. The occlusionmeasurement O is expressed as a percentage according to the followingformula:

$O = {{\frac{{Invalid}\mspace{14mu} {area}\mspace{14mu} {in}\mspace{14mu} {the}\mspace{14mu} {segmentation}\mspace{14mu} {mask}}{{Segmentation}\mspace{14mu} {mask}\mspace{14mu} {size}}100}{\% \;.}}$

Likewise, the dilation measurement generator 30 receives thedimensionless data of the iris image and its mask to measure the amountof iris dilation. If the iris is too dilated, insufficient informationis available in the image for recognition. The dilation measurement D iscalculated as:

$D = {{\frac{{Pupil}\mspace{14mu} {radius}}{{Iris}\mspace{14mu} {radius}}100}{\% \;.}}$

The measurements obtained by the feature information measurementgenerator 24, the occlusion measurement generator 28, and the dilationmeasurement generator 30 are provided to the quality score generator 34.These measurements are combined to produce a single quality score forthe iris image. The quality score Q is determined as Q=f(FIM)·g(O)·h(D),where f( ), g( ), and h( ) are normalization functions. The function fnormalizes the FIM score from 0 to 1 as follows:

${f({FIM})} = \left\{ \begin{matrix}{{\alpha \cdot {FIM}},} & {0 \leq {FIM} \leq \beta} \\{1,} & {{FIM} > \beta}\end{matrix} \right.$

and β=0.48 and α=1/β. Because the relationship between available irispatterns and iris recognition accuracy is not linear, the function g iscalculated as g(O)=1−Ke^(λO), where k=0.011 and λ=4.5. A plot of thefunction g is shown in FIG. 6. Dilation is also non-linear and thefunction h is calculated as h(D)=ξe^(−γD), where ξ=1×10⁻⁶ and γ=25. Inapplication of the above described system and method to other types ofbiometric data, occlusion may be understood as referring to obstructionsto portions of the image on its outer boundary while dilation may beunderstood as referring to obstructions within the interior of theimage.

Those skilled in the art will recognize that numerous modifications canbe made to the specific implementations described above. While theembodiments above have been described with reference to specificapplications, embodiments addressing other applications may be developedwithout departing from the principles of the invention described above.For example, while the image quality evaluation system has beendescribed as being used with an iris recognition system, it may also beused with other identification systems that use biometric image data.Therefore, the following claims are not to be limited to the specificembodiments illustrated and described above. The claims, as originallypresented and as they may be amended, encompass variations,alternatives, modifications, improvements, equivalents, and substantialequivalents of the embodiments and teachings disclosed herein, includingthose that are presently unforeseen or unappreciated, and that, forexample, may arise from applicants/patentees and others.

1. A method for measuring quality of a biometric image comprising:converting a biometric image to dimensionless image data; filtering thedimensionless image data with a band pass filter; identifying aplurality of portions in the filtered data as containing identificationfeatures, each portion in the plurality having an information distancethat indicates feature content greater than portions in the filtereddata that are excluded from the plurality; and measuring clarity for thebiometric image from the identified plurality of portions in thefiltered data.
 2. The method of claim 1, the biometric image conversionfurther comprising: converting biometric image data into polarcoordinate image data.
 3. The method of claim 1, the biometric imageconversion further comprising: converting biometric image data intolog-polar coordinate image data.
 4. The method of claim 1, the filteringof the dimensionless data further comprising: applying a Log-Gaborfilter to the dimensionless data.
 5. The method of claim 2 furthercomprising: converting the dimensionless image data to spatial domaindata; and converting the spatial domain data to dimensionless datafollowing application of a Log-Gabor band pass filter to the spatialdomain data.
 6. The method of claim 1, the filtered data portionidentification further comprising: applying a sliding window to thefiltered image data to identify portions for information distancecalculations; and generating an information distance for the portions ofthe filtered data identified by the sliding window.
 7. The method ofclaim 6, the filtered data portion identification further comprising:weighting information distances for selected portions identified by thesliding window.
 8. The method of claim 7, the filtered data portionidentification further comprising: ranking the weighted informationdistances; and selecting a number N portions corresponding to the Nweighted information distances that are larger than the other weightedinformation distances in the ranking of the weighted informationdistances.
 9. The method of claim 1 further comprising: measuringocclusion in the dimensionless biometric image.
 10. The method of claim9 further comprising: measuring dilation in the dimensionless biometricimage.
 11. The method of claim 10 further comprising: generating aquality measurement for the biometric image from the claritymeasurement, the occlusion measurement, and the dilation measurement.12. A system for measuring quality of a biometric image comprising: apreprocessor configured to convert biometric image data to dimensionlessimage data; a feature extractor configured to filter the dimensionlessdata with a band pass filter; a region of interest selector configuredto identify a plurality of portions of the filtered image data, eachportion in the plurality having an information distance that isindicative of feature content; and a feature information measurementgenerator configured to measure clarity of the biometric image from theportions in the plurality of portions.
 13. The system of claim 12, thepreprocessor being configured to convert the biometric image data to oneof polar coordinate data and log-polar coordinate data.
 14. The systemof claim 13, the feature extractor further comprising: a domainconverter configured to convert the dimensionless data in the polarcoordinates or the log-polar coordinates to a spatial domain; and aninverse domain generator configured to convert the filtered data to thedimensionless coordinates from which it was converted for filtration.15. The system of claim 14, the feature extractor being furtherconfigured to apply a Log-Gabor filter to the spatial domain data. 16.The system of claim 15, the region of interest selector furthercomprising: a sliding window to identify portions of the filtereddimensionless data for information distance calculations; and ainformation distance generator for measuring an information distance forthe portions identified by application of the sliding window to thefiltered dimensionless data.
 17. The system of claim 15, the region ofinterest selector being further configured to weight the informationdistances for selected portions of the plurality.
 18. The system ofclaim 17, the feature selector being further configured to rank theweighted information distances measured for the portions in theplurality.
 19. The system of claim 12 further comprising: an occlusionmeasurement generator configured to measure occlusion in the biometricdimensionless image data; and a dilation measurement generatorconfigured to measure dilation in the biometric dimensionless imagedata.
 20. The system of claim 19 further comprising: an image qualitymeasurement generator for generating a quality measurement for thebiometric image from the clarity measurement, the occlusion measurement,and the dilation measurement.